Modelling biological systems
Modelling biological systems is a significant task of
An unexpected
Standards
By far the most widely accepted standard format for storing and exchanging models in the field is the
Particular tasks
Cellular model
Creating a cellular model has been a particularly challenging task of
The complex network of biochemical reaction/transport processes and their spatial organization make the development of a predictive model of a living cell a grand challenge for the 21st century, listed as such by the National Science Foundation (NSF) in 2006.[5]
A whole cell computational model for the bacterium Mycoplasma genitalium, including all its 525 genes, gene products, and their interactions, was built by scientists from Stanford University and the J. Craig Venter Institute and published on 20 July 2012 in Cell.[6]
A dynamic computer model of intracellular signaling was the basis for Merrimack Pharmaceuticals to discover the target for their cancer medicine MM-111.[7]
Membrane computing is the task of modelling specifically a cell membrane.
Multi-cellular organism simulation
An open source simulation of C. elegans at the cellular level is being pursued by the OpenWorm community. So far the physics engine Gepetto has been built and models of the neural connectome and a muscle cell have been created in the NeuroML format.[8]
Protein folding
Protein structure prediction is the prediction of the three-dimensional structure of a
Human biological systems
Brain model
The
Model of the immune system
The last decade has seen the emergence of a growing number of simulations of the immune system.[14][15]
Virtual liver
The Virtual Liver project is a 43 million euro research program funded by the German Government, made up of seventy research group distributed across Germany. The goal is to produce a virtual liver, a dynamic mathematical model that represents human liver physiology, morphology and function.[16]
Tree model
Electronic trees (e-trees) usually use
Ecological models
Ecosystem models are mathematical representations of ecosystems. Typically they simplify complex foodwebs down to their major components or trophic levels, and quantify these as either numbers of organisms, biomass or the inventory/concentration of some pertinent chemical element (for instance, carbon or a nutrient species such as nitrogen or phosphorus).
Models in ecotoxicology
The purpose of models in ecotoxicology is the understanding, simulation and prediction of effects caused by toxicants in the environment. Most current models describe effects on one of many different levels of biological organization (e.g. organisms or populations). A challenge is the development of models that predict effects across biological scales. Ecotoxicology and models discusses some types of ecotoxicological models and provides links to many others.
Modelling of infectious disease
It is possible to model the progress of most infectious diseases mathematically to discover the likely outcome of an
See also
- Biological data visualization
- Biosimulation
- Gillespie algorithm
- Molecular modelling software
- Stochastic simulation
Notes
References
- ^ Andres Kriete, Roland Eils, Computational Systems Biology, Elsevier Academic Press, 2006.
- S2CID 52922135.
- ^ Klipp, Liebermeister, Helbig, Kowald and Schaber. (2007). "Systems biology standards—the community speaks" (2007), Nature Biotechnology 25(4):390–391.
- PMID 25404136.
- ^ American Association for the Advancement of Science
- ^ Karr, J. (2012) A Whole-Cell Computational Model Predicts Phenotype from Genotype Cell
- ^ McDonagh, CF (2012) Antitumor Activity of a Novel Bispecific Antibody That Targets the ErbB2/ErbB3 Oncogenic Unit and Inhibits Heregulin-Induced Activation of ErbB3. Molecular Cancer Therapeutics
- ^ OpenWorm Downloads
- ^ Graham-Rowe, Duncan. "Mission to build a simulated brain begins", NewScientist, June 2005.
- ^ Palmer, Jason. Simulated brain closer to thought, BBC News.
- ^ The Human Brain Project. Archived July 5, 2012, at the Wayback Machine
- ^ Video of Henry Markram presenting The Human Brain Project on 22 June 2012.
- ^ FET Flagships Initiative homepage.
- ISBN 978-3-540-22123-4.
- ^ "Computer Simulation Captures Immune Response To Flu". Retrieved 2009-08-19.
- ^ "Virtual Liver Network". Archived from the original on 2012-09-30. Retrieved 2016-10-14.
- ^ "Simulating plant growth". Archived from the original on 2009-12-09. Retrieved 2009-10-18.
Sources
- Antmann, S. S.; Marsden, J. E.; Sirovich, L., eds. (2009). Mathematical Physiology (2nd ed.). New York, New York: Springer. ISBN 978-0-387-75846-6.
- Barnes, D.J.; Chu, D. (2010), Introduction to Modelling for Biosciences, Springer Verlag
- An Introduction to Infectious Disease Modelling by Emilia Vynnycky and Richard G White. An introductory book on infectious disease modelling and its applications.
Further reading
- Barab, A. -L.; Oltvai, Z. (2004). "Network biology* understanding the cell's functional organization". Nature Reviews Genetics. 5 (2): 101–113. S2CID 10950726.
- Covert; Schilling, C.; Palsson, B. (2001). "Regulation of gene expression in flux balance models of metabolism". Journal of Theoretical Biology. 213 (1): 73–88. PMID 11708855.
- Covert, M. W.; Palsson, B. . (2002). "Transcriptional regulation in constraints-based metabolic models of Escherichia coli". The Journal of Biological Chemistry. 277 (31): 28058–28064. PMID 12006566.
- Edwards; Palsson, B. (2000). "The Escherichia coli MG1655 in silico metabolic genotype* its definition, characteristics, and capabilities". Proceedings of the National Academy of Sciences of the United States of America. 97 (10): 5528–5533. PMID 10805808.
- Bonneau, R. (2008). "Learning biological networks* from modules to dynamics". Nature Chemical Biology. 4 (11): 658–664. PMID 18936750.
- Edwards, J. S.; Ibarra, R. U.; Palsson, B. O. (2001). "In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data". Nature Biotechnology. 19 (2): 125–130. S2CID 1619105.
- Fell, D. A. (1998). "Increasing the flux in metabolic pathways* A metabolic control analysis perspective". Biotechnology and Bioengineering. 58 (2–3): 121–124. PMID 10191380.
- Hartwell, L. H.; Hopfield, J. J.; Leibler, S.; Murray, A. W. (1999). "From molecular to modular cell biology". Nature. 402 (6761 Suppl): C47–C52. S2CID 34290973.
- Ideker; Galitski, T.; Hood, L. (2001). "A new approach to decoding life* systems biology". Annual Review of Genomics and Human Genetics. 2 (1): 343–372. S2CID 922378.
- Kitano, H. (2002). "Computational systems biology". Nature. 420 (6912): 206–210. S2CID 4401115.
- Kitano, H. (2002). "Systems biology* a brief overview". Science. 295 (5560): 1662–1664. S2CID 2703843.
- Kitano (2002). "Looking beyond the details* a rise in system-oriented approaches in genetics and molecular biology". Current Genetics. 41 (1): 1–10. S2CID 18976498.
- Gilman, A. G.; Simon, M. I.; Bourne, H. R.; Harris, B. A.; Long, R.; Ross, E. M.; Stull, J. T.; Taussig, R.; Bourne, H. R.; Arkin, A. P.; Cobb, M. H.; Cyster, J. G.; Devreotes, P. N.; Ferrell, J. E.; Fruman, D.; Gold, M.; Weiss, A.; Stull, J. T.; Berridge, M. J.; Cantley, L. C.; Catterall, W. A.; Coughlin, S. R.; Olson, E. N.; Smith, T. F.; Brugge, J. S.; Botstein, D.; Dixon, J. E.; Hunter, T.; Lefkowitz, R. J.; Pawson, A. J. (2002). "Overview of the Alliance for Cellular Signaling" (PDF). Nature. 420 (6916): 703–706. S2CID 4367083.
- Palsson, Bernhard (2006). Systems biology* properties of reconstructed networks. Cambridge: Cambridge University Press. ISBN 978-0-521-85903-5.
- Kauffman; Prakash, P.; Edwards, J. S. (2003). "Advances in flux balance analysis". Current Opinion in Biotechnology. 14 (5): 491–496. PMID 14580578.
- Segrè, D.; Vitkup, D.; Church, G. M. (2002). "Analysis of optimality in natural and perturbed metabolic networks". Proceedings of the National Academy of Sciences of the United States of America. 99 (23): 15112–15117. PMID 12415116.
- Wildermuth, MC (2000). "Metabolic control analysis* biological applications and insights". Genome Biology. 1 (6): REVIEWS1031. PMID 11178271.